TY - GEN
T1 - Feature learning for SAR images using convolutional neural network
AU - Liu, Qi
AU - Li, Shaojie
AU - Mei, Shaohui
AU - Jiang, Ruoqiao
AU - Li, Jieqi
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/31
Y1 - 2018/10/31
N2 - Convolutional neural network (CNN) has been widely used in many research areas due to its powerful ability of feature learning. In this paper, the powerful ability of feature learning in CNN is explored by constructing a novel convolutional network (ConvNet) for SAR image processing. The proposed ConvNet is firstly trained under classification task, in which effective features can be learned automatically from the training data. Specifically, data argument is adopted to overcome the small-sample-problem in SAR images. When well-trained, the proposed ConvNet can be directly used for feature extraction of other images, even though their classes maybe not used in the training. Experimental results on benchmark MSTAR data set demonstrate that the proposed ConvNet is effective for classification of SAR images, and the features learned from it are more effective than traditional hand-crafted features in SAR image processing.
AB - Convolutional neural network (CNN) has been widely used in many research areas due to its powerful ability of feature learning. In this paper, the powerful ability of feature learning in CNN is explored by constructing a novel convolutional network (ConvNet) for SAR image processing. The proposed ConvNet is firstly trained under classification task, in which effective features can be learned automatically from the training data. Specifically, data argument is adopted to overcome the small-sample-problem in SAR images. When well-trained, the proposed ConvNet can be directly used for feature extraction of other images, even though their classes maybe not used in the training. Experimental results on benchmark MSTAR data set demonstrate that the proposed ConvNet is effective for classification of SAR images, and the features learned from it are more effective than traditional hand-crafted features in SAR image processing.
KW - Classification
KW - Convolutional neural network (CNN)
KW - Feature extraction
KW - Feature learning
KW - Synthetic Aperture Radar (SAR)
UR - http://www.scopus.com/inward/record.url?scp=85064275119&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2018.8519159
DO - 10.1109/IGARSS.2018.8519159
M3 - 会议稿件
AN - SCOPUS:85064275119
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 7003
EP - 7006
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Y2 - 22 July 2018 through 27 July 2018
ER -